


import math
import sys
def 							UpperCamelCase_(					_A :str	)->		str:
			UpperCamelCase__       =							""
			try:
						with open(_A					,  "rb"	) as binary_file:
									UpperCamelCase__       =							binary_file.read()
						for dat in data:
									UpperCamelCase__       =							F'''{dat:08b}'''
									result += curr_byte
						return result
			except OSError:
						print("File not accessible"	)
						sys.exit()
def 							UpperCamelCase_(					_A :str	)->		str:
			UpperCamelCase__       =							{"0": "0", "1": "1"}
			UpperCamelCase__,   UpperCamelCase__       =							"", ""
			UpperCamelCase__       =							len(_A	)

			for i in range(len(_A	)	):
						curr_string += data_bits[i]
						if curr_string not in lexicon:
									continue

						UpperCamelCase__       =							lexicon[curr_string]
						result += last_match_id
						UpperCamelCase__       =							last_match_id + "0"

						if math.loga(_A	).is_integer():
									UpperCamelCase__       =							{}
									for curr_key in list(_A	):
												UpperCamelCase__       =							lexicon.pop(_A	)
									UpperCamelCase__       =							new_lex

						UpperCamelCase__       =							last_match_id + "1"
						index += 1
						UpperCamelCase__       =							""
			return result
def 							UpperCamelCase_(					_A :str					,  _A :str	)->		None:
			UpperCamelCase__       =							8
			try:
						with open(_A					,  "wb"	) as opened_file:
									UpperCamelCase__       =							[
									    to_write[i : i + byte_length]
									    for i in range(0					,  len(_A	)					,  _A	)
									]

									if len(result_byte_array[-1]	) % byte_length == 0:
												result_byte_array.append("10000000"	)
									else:
												result_byte_array[-1] += "1" + "0" * (
												    byte_length - len(result_byte_array[-1]	) - 1
												)

									for elem in result_byte_array[:-1]:
												opened_file.write(int(_A					,  2	).to_bytes(1					,  byteorder="big"	)	)
			except OSError:
						print("File not accessible"	)
						sys.exit()
def 							UpperCamelCase_(					_A :str	)->		str:
			UpperCamelCase__       =							0
			for letter in data_bits:
						if letter == "1":
									break
						counter += 1

			UpperCamelCase__       =							data_bits[counter:]
			UpperCamelCase__       =							data_bits[counter + 1 :]
			return data_bits
def 							UpperCamelCase_(					_A :str					,  _A :str	)->		None:
			UpperCamelCase__       =							read_file_binary(_A	)
			UpperCamelCase__       =							remove_prefix(_A	)
			UpperCamelCase__       =							decompress_data(_A	)
			write_file_binary(_A					,  _A	)


if __name__ == "__main__":
						compress(sys.argv[1], sys.argv[2])






import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors







@require_tokenizers
class 							lowerCamelCase__						(				UpperCAmelCase       ,						unittest.TestCase   ):






			"""simple docstring"""






			_UpperCamelCase		:       List[str] 			=		MvpTokenizer
			_UpperCamelCase		:       Any 			=		MvpTokenizerFast
			_UpperCamelCase		:       List[str] 			=		True
			_UpperCamelCase		:       Dict 			=		filter_roberta_detectors




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						super().setUp()
						UpperCamelCase__       =							[
						    "l",
						    "o",
						    "w",
						    "e",
						    "r",
						    "s",
						    "t",
						    "i",
						    "d",
						    "n",
						    "\u0120",
						    "\u0120l",
						    "\u0120n",
						    "\u0120lo",
						    "\u0120low",
						    "er",
						    "\u0120lowest",
						    "\u0120newer",
						    "\u0120wider",
						    "<unk>",
						]
						UpperCamelCase__       =							dict(zip(snake_case   ,    range(len(snake_case  )  )  )  )
						UpperCamelCase__       =							["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
						UpperCamelCase__       =							{"unk_token": "<unk>"}

						UpperCamelCase__       =							os.path.join(self.tmpdirname   ,    VOCAB_FILES_NAMES["vocab_file"]  )
						UpperCamelCase__       =							os.path.join(self.tmpdirname   ,    VOCAB_FILES_NAMES["merges_file"]  )
						with open(self.vocab_file   ,    "w"   ,    encoding="utf-8"  ) as fp:
									fp.write(json.dumps(snake_case  ) + "\n"  )
						with open(self.merges_file   ,    "w"   ,    encoding="utf-8"  ) as fp:
									fp.write("\n".join(snake_case  )  )




			def 					snake_case__					(				self   ,    **snake_case  ):

						'''simple docstring'''


						kwargs.update(self.special_tokens_map  )
						return self.tokenizer_class.from_pretrained(self.tmpdirname   ,    **snake_case  )




			def 					snake_case__					(				self   ,    **snake_case  ):

						'''simple docstring'''


						kwargs.update(self.special_tokens_map  )
						return self.rust_tokenizer_class.from_pretrained(self.tmpdirname   ,    **snake_case  )




			def 					snake_case__					(				self   ,    snake_case  ):

						'''simple docstring'''


						return "lower newer", "lower newer"




			@cached_property
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						return MvpTokenizer.from_pretrained("RUCAIBox/mvp"  )




			@cached_property
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						return MvpTokenizerFast.from_pretrained("RUCAIBox/mvp"  )




			@require_torch
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							["A long paragraph for summarization.", "Another paragraph for summarization."]
						UpperCamelCase__       =							[0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									UpperCamelCase__       =							tokenizer(snake_case   ,    max_length=len(snake_case  )   ,    padding=snake_case   ,    return_tensors="pt"  )
									self.assertIsInstance(snake_case   ,    snake_case  )

									self.assertEqual((2, 9)   ,    batch.input_ids.shape  )
									self.assertEqual((2, 9)   ,    batch.attention_mask.shape  )
									UpperCamelCase__       =							batch.input_ids.tolist()[0]
									self.assertListEqual(snake_case   ,    snake_case  )
									# Test that special tokens are reset




			@require_torch
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							["A long paragraph for summarization.", "Another paragraph for summarization."]
						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									UpperCamelCase__       =							tokenizer(snake_case   ,    padding=snake_case   ,    return_tensors="pt"  )
									# check if input_ids are returned and no labels
									self.assertIn("input_ids"   ,    snake_case  )
									self.assertIn("attention_mask"   ,    snake_case  )
									self.assertNotIn("labels"   ,    snake_case  )
									self.assertNotIn("decoder_attention_mask"   ,    snake_case  )




			@require_torch
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							[
						    "Summary of the text.",
						    "Another summary.",
						]
						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									UpperCamelCase__       =							tokenizer(text_target=snake_case   ,    max_length=32   ,    padding="max_length"   ,    return_tensors="pt"  )
									self.assertEqual(32   ,    targets["input_ids"].shape[1]  )




			@require_torch
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									UpperCamelCase__       =							tokenizer(
									    ["I am a small frog" * 1024, "I am a small frog"]   ,    padding=snake_case   ,    truncation=snake_case   ,    return_tensors="pt"  )
									self.assertIsInstance(snake_case   ,    snake_case  )
									self.assertEqual(batch.input_ids.shape   ,    (2, 1024)  )




			@require_torch
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							["A long paragraph for summarization."]
						UpperCamelCase__       =							[
						    "Summary of the text.",
						]
						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									UpperCamelCase__       =							tokenizer(snake_case   ,    text_target=snake_case   ,    return_tensors="pt"  )
									UpperCamelCase__       =							inputs["input_ids"]
									UpperCamelCase__       =							inputs["labels"]
									self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()  )
									self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()  )
									self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()  )
									self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						pass







			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''  ):
												UpperCamelCase__       =							self.rust_tokenizer_class.from_pretrained(snake_case   ,    **snake_case  )
												UpperCamelCase__       =							self.tokenizer_class.from_pretrained(snake_case   ,    **snake_case  )
												UpperCamelCase__       =							"A, <mask> AllenNLP sentence."
												UpperCamelCase__       =							tokenizer_r.encode_plus(snake_case   ,    add_special_tokens=snake_case   ,    return_token_type_ids=snake_case  )
												UpperCamelCase__       =							tokenizer_p.encode_plus(snake_case   ,    add_special_tokens=snake_case   ,    return_token_type_ids=snake_case  )

												# token_type_ids should put 0 everywhere
												self.assertEqual(sum(tokens_r["token_type_ids"]  )   ,    sum(tokens_p["token_type_ids"]  )  )

												# attention_mask should put 1 everywhere, so sum over length should be 1
												self.assertEqual(
												    sum(tokens_r["attention_mask"]  ) / len(tokens_r["attention_mask"]  )   ,    sum(tokens_p["attention_mask"]  ) / len(tokens_p["attention_mask"]  )   ,    )

												UpperCamelCase__       =							tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]  )
												UpperCamelCase__       =							tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]  )

												# Rust correctly handles the space before the mask while python doesnt
												self.assertSequenceEqual(tokens_p["input_ids"]   ,    [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]  )
												self.assertSequenceEqual(tokens_r["input_ids"]   ,    [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]  )

												self.assertSequenceEqual(
												    snake_case   ,    ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]  )
												self.assertSequenceEqual(
												    snake_case   ,    ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]  )


